A food quality detection method based on electronic nose technology

电子鼻 质量(理念) 计算机科学 模式识别(心理学) 计算机视觉 人工智能 物理 量子力学
作者
Mingyang Wang,Yinsheng Chen,Deyun Chen,Xinchun Tian,Wenjie Zhao,Yunbo Shi
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (5): 056004-056004 被引量:5
标识
DOI:10.1088/1361-6501/ad29e4
摘要

Abstract Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of convolutional neural network combined with wavelet scattering network (CNN-WSN) and improved seahorse optimizes kernel extreme learning machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of CNN are fused with the scattering features of WSN, and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the seahorse optimisation algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the KELM model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.
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